Advances in Bayesian inference and stable optimization for large-scale machine learning problems

A core task in machine learning, and the topic of this thesis, is developing faster and more accurate methods of posterior inference in probabilistic models. The thesis has two components. The first explores using deterministic methods to improve the efficiency of Markov Chain Monte Carlo (MCMC) algorithms. We propose new MCMC algorithms that can use deterministic methods as a “prior” to bias MCMC proposals to be in areas of high posterior density, leading to highly efficient sampling. In Chapter 2 we develop such methods for continuous distributions, and in Chapter 3 for binary distributions. The resulting methods consistently outperform existing state-of-the-art sampling techniques, sometimes by several orders of magnitude. Chapter 4 uses similar ideas as in Chapters 2 and 3, but in the context of modeling the performance of left-handed players in one-on-one interactive sports. The second part of this thesis explores the use of stable stochastic gradient descent (SGD) methods for computing a maximum a posteriori (MAP) estimate in large-scale machine learning problems. In Chapter 5 we propose two such methods for softmax regression. The first is an implementation of Implicit SGD (ISGD), a stable but difficult to implement SGD method, and the second is a new SGD method specifically designed for optimizing a double-sum formulation of the softmax. Both methods comprehensively outperform the previous state-of-the-art on seven real world datasets. Inspired by the success of ISGD on the softmax, we investigate its application to neural networks in Chapter 6. In this chapter we present a novel layer-wise approximation of ISGD that has efficiently computable updates. Experiments show that the resulting method is more robust to high learning rates and generally outperforms standard backpropagation on a variety of tasks

Gibbs sampling with people

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications

Gibbs sampling with people

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications

On Building Generalizable Learning Agents

It has been a long-standing goal in Artificial Intelligence (AI) to build machines that can solve tasks that humans can. Thanks to the recent rapid progress in data-driven methods, which train agents to solve tasks by learning from massive training data, there have been many successes in applying such learning approaches to handle and even solve a number of extremely challenging tasks, including image classification, language generation, robotics control, and several multi-player games. The key factor for all these data-driven successes is that the trained agents can generalize to test scenarios that are unseen during training. This generalization capability is the foundation for building any practical AI system. This thesis studies generalization, the fundamental challenge in AI, and proposes solutions to improve the generalization performances of learning agents in a variety of problems. We start by providing a formal formulation of the generalization problem in the context of reinforcement learning and proposing 4 principles within this formulation to guide the design of training techniques for improved generalization. We validate the effectiveness of our proposed principles by considering 4 different domains, from simple to complex, and developing domain-specific techniques following these principles. Particularly, we begin with the simplest domain, i.e., path-finding on graphs (Part I), and then consider visual navigation in a 3D world (Part II) and competition in complex multi-agent games (Part III), and lastly tackle some natural language processing tasks (Part IV). Empirical evidences demonstrate that the proposed principles can generally lead to much improved generalization performances in a wide range of problems

Fabric of Memory: A Multimedia Synthesis of The History of Cockatoo Island

Fabric of Memory is a creative exploration of the history of Cockatoo Island. This practice-based research project thesis comprises a written dissertation and an artistic multimedia work. The written dissertation explores the concept of identity and connection to site as a poetic idiom in multimedia composition. I will explore how my research into cultural and historical perspectives of the island has informed my creative response to the site. In documenting the creation of this composition, I hope to offer new insights into sonic exploration, multimodal artistic expression, and strategies to engage with place. Fabric of Memory comprises a long-form multimedia composition that explores four eras of Cockatoo Island. The Pre-European-Indigenous era, colonial era, Industrial and my personal experience of living surrounded by a working shipyard as an island resident for eighteen years. Each composition shares a consistent conceptual approach of reconnection to place and identity via the inclusion of site sound as a component of the work. The multimodal composition aims to create an audience experience that opens new possibilities and dialogue around history, culture, and our place in the material landscape. An important outcome is to produce a strategy for other historical sites. A context of composition, sound design, and multimedia for other places to create content and improve audience engagement with the lands on which they live. I propose the concept of an ‘empathic bridge’ as a model to enhance audience engagement with the site

When autoencoders meet recommender systems : COFILS approach

Collaborative Filtering to Supervised Learning (COFILS) transforms a Collaborative Filtering (CF) problem into classical Supervised Learning (SL) problem. Applying COFILS reduce data sparsity and make it possible to test a variety of SL algorithms rather than matrix decomposition methods. It main steps are: extraction, mapping and prediction. Firstly, a Singular Value Decomposition (SVD) generates a set of latent variables from a ratings matrix. Next, on the mapping phase, a new data set is generated where each sample contains a set of latent variables from an user and it rated item; and a target that corresponds the user rating for that item. Finally, on the last phase, a SL algorithm is applied. One problem of COFILS is it’s dependency on SVD, that is not able to extract non-linear features from data and it is not robust to noisy data. To address this problem, we propose switching SVD to a Stacked Denoising Autoencoder (SDA) on the first phase of COFILS. With SDA, more useful and complex representations can be learned in a Deep Network with a local denoising criterion. We test our novel technique, namely Deep Learning COFILS (DL-COFILS), on MovieLens, R3 Yahoo! Music and Movie Tweetings data sets and compare to COFILS, as a baseline, and state of the art CF techniques. Our results indicate that DL-COFILS outperforms COFILS for all the data sets and with an improvement up to 5.9%. Also, DL-COFILS achieves the best result for the MovieLens 100k data set and ranks on the top three algorithms for these data sets. Thus, we show that DL-COFILS represents an advance on COFILS methodology, improving it’s results and that is a suitable method for CF problem.Collaborative Filtering to Supervised Learning (COFILS) transforma um problema de filtragem colaborativa (CF) em um problema clássico de aprendizado supervisionado (SL). Sua aplicação reduz a esparsidade e torna possível a utilização de variados algoritmos de SL em oposição aos métodos de decomposição de matrizes. Primeiramente, a Decomposição em Valores Singulares (SVD) gera um conjunto de variáveis latentes a partir da matriz de avaliações. Na fase de mapeamento, um novo conjunto de dados é gerado, do qual cada amostra contém um conjunto de variáveis latentes de um usuário e do item avaliado; e um valor que corresponde a avaliação que o usuário atribuiu a esse item. Por fim, o algoritmo de SL é aplicado. Um ponto negativo do COFILS é sua dependência ao SVD, incapaz de extrair características não-lineares e sem robustez `a dados ruidosos. Nesse caso, propomos a troca do SVD por um Stacked Denoising Autoencoder (SDA). Com o uso de um SDA, representações mais úteis e complexas podem ser aprendidas em uma rede neural profunda com um critério local de remoção de ruído. Executamos nossa técnica, chamada Deep Learning COFILS (DL-COFILS), nos conjuntos de dados MovieLens, R3 Yahoo! Music e Movie Tweetings comparando os resultados com o COFILS padrão, como baseline, e demais técnicas de estado da arte de CF. Com os resultados obtidos, é possível mencionar que DL-COFILS supera COFILS para todos os conjuntos de dados, com uma melhora de até 5.9%. Além disso, o DLCOFILS alcança o melhor resultado para o MovieLens 100k e se encontra entre os três melhores algoritmos nos demais conjuntos de dados. Dessa forma, mostraremos que DL-COFILS representa um avanço na metodologia COFILS, melhorando seus resultados e se mostrando um método adequado para CF

Audio-visual football video analysis, from structure detection to attention analysis

Sport video is an important video genre. Content-based sports video analysis attracts great interest from both industry and academic fields. A sports video is characterised by repetitive temporal structures, relatively plain contents, and strong spatio-temporal variations, such as quick camera switches and swift local motions. It is necessary to develop specific techniques for content-based sports video analysis to utilise these characteristics. For an efficient and effective sports video analysis system, there are three fundamental questions: (1) what are key stories for sports videos; (2) what incurs viewer’s interest; and (3) how to identify game highlights. This thesis is developed around these questions. We approached these questions from two different perspectives and in turn three research contributions are presented, namely, replay detection, attack temporal structure decomposition, and attention-based highlight identification. Replay segments convey the most important contents in sports videos. It is an efficient approach to collect game highlights by detecting replay segments. However, replay is an artefact of editing, which improves with advances in video editing tools. The composition of replay is complex, which includes logo transitions, slow motions, viewpoint switches and normal speed video clips. Since logo transition clips are pervasive in game collections of FIFA World Cup 2002, FIFA World Cup 2006 and UEFA Championship 2006, we take logo transition detection as an effective replacement of replay detection. A two-pass system was developed, including a five-layer adaboost classifier and a logo template matching throughout an entire video. The five-layer adaboost utilises shot duration, average game pitch ratio, average motion, sequential colour histogram and shot frequency between two neighbouring logo transitions, to filter out logo transition candidates. Subsequently, a logo template is constructed and employed to find all transition logo sequences. The precision and recall of this system in replay detection is 100% in a five-game evaluation collection. An attack structure is a team competition for a score. Hence, this structure is a conceptually fundamental unit of a football video as well as other sports videos. We review the literature of content-based temporal structures, such as play-break structure, and develop a three-step system for automatic attack structure decomposition. Four content-based shot classes, namely, play, focus, replay and break were identified by low level visual features. A four-state hidden Markov model was trained to simulate transition processes among these shot classes. Since attack structures are the longest repetitive temporal unit in a sports video, a suffix tree is proposed to find the longest repetitive substring in the label sequence of shot class transitions. These occurrences of this substring are regarded as a kernel of an attack hidden Markov process. Therefore, the decomposition of attack structure becomes a boundary likelihood comparison between two Markov chains. Highlights are what attract notice. Attention is a psychological measurement of “notice ”. A brief survey of attention psychological background, attention estimation from vision and auditory, and multiple modality attention fusion is presented. We propose two attention models for sports video analysis, namely, the role-based attention model and the multiresolution autoregressive framework. The role-based attention model is based on the perception structure during watching video. This model removes reflection bias among modality salient signals and combines these signals by reflectors. The multiresolution autoregressive framework (MAR) treats salient signals as a group of smooth random processes, which follow a similar trend but are filled with noise. This framework tries to estimate a noise-less signal from these coarse noisy observations by a multiple resolution analysis. Related algorithms are developed, such as event segmentation on a MAR tree and real time event detection. The experiment shows that these attention-based approach can find goal events at a high precision. Moreover, results of MAR-based highlight detection on the final game of FIFA 2002 and 2006 are highly similar to professionally labelled highlights by BBC and FIFA